The Prevalence and Risk Factors of Diabetic Retinopathy: Screening and Prophylaxis Project in 6 Provinces of China

Association between multiple metals mixture and diabetic retinopathy in older adults with diabetes mellitus: a cross-sectional study in China

Previous studies have linked single metal with diabetic retinopathy (DR), but information about the combined effects of multiple metals mixture was scarce. Thus, we performed this cross-sectional study to investigate the single and joint associations between multiple metals mixture and DR risk among elderly diabetic population in China. A total of 1127 elderly adults (aged ≥ 60) with diabetes mellitus from a large-scale DR screening program in southern China included. Metals (beryllium, magnesium, chromium, manganese, iron, nickel, copper, arsenic, thallium and lead) in serum were quantified by inductively coupled plasma mass spectrometer. DR was diagnosed according to the consensus of the global DR project group. The relationships between metals and DR risks were estimated by logistic regression, Bayesian kernel machine regression (BKMR) and weighted quantile sum (WQS) regression. Of 1127 older adults with diabetes mellitus, there were 324 DR and 803 non-DR participants. Logistic regression models found serum magnesium and iron were negatively related to DR risks. Both BKMR model and WQS regression revealed that higher serum levels of multiple metals mixture were associated with lower risks of DR, with Be contributing the most to the overall effect. Additionally, in subgroup analyses, the interaction between beryllium and blood pressure on DR risk was also observed (Pinteraction = 0.008). Overall, these results provided new evidence of direct association between multiple metals mixture and DR risk among elderly diabetic population in China.

The relationship between weight-adjusted-waist index, body mass index and diabetic retinopathy among American adults: a population-based analysis

Diabetic retinopathy (DR) is a common complication of diabetes, with its prevalence increasing globally. While previous research has linked obesity indices such as body mass index (BMI) to DR, the association with weight-adjusted-waist index (WWI) remains unclear. Additionally, the relationship between WWI and DR has not been fully elucidated. This cross-sectional study analyzed data from the National Health and Nutrition Examination Survey (2005-2008) to investigate these associations in Americans aged 40 and above. The study included 5436 participants (2705 men and 2731 women). Weighted logistic regression analysis revealed a significant increase in DR prevalence with higher WWI and BMI values. Smooth curve analysis demonstrated a linear correlation between WWI and DR. The findings suggest that both WWI and BMI are independently associated with DR risk among older US adults, highlighting the importance of considering central obesity measures in assessing diabetic complications.

Association between AST/ALT ratio and diabetic retinopathy risk in type 2 diabetes: a cross-sectional investigation

Objective

This study aimed to explore the association between the aspartate aminotransferase to alanine aminotransferase ratio (AST/ALT ratio) and diabetic retinopathy (DR) in patients with type 2 diabetes.

Methods

In this cross-sectional study, clinical data from 3002 patients with type 2 diabetes admitted to the Department of Endocrinology of our hospital between January 1, 2021, and December 1, 2022, were retrospectively collected. Measurements of AST and ALT were conducted and diabetes-related complications were screened. The association between AST/ALT ratio and diabetic retinopathy was assessed using multivariate logistic regression, and a generalized additive model (GAM) was used to investigate nonlinear relationships. Subgroup analyses and interaction tests were also conducted.

Results

Among the 3002 patients, 1590 (52.96%) were male and 1412 (47.04%) were female. The mean AST/ALT ratio was 0.98 ± 0.32, ranging from 0.37 (Min) to 2.17 (Max). Diabetic retinopathy was present in 40.47% of the patients. After multivariate adjustments, for each 0.1 unit increase in AST/ALT ratio, the risk of DR increased by 4% (OR = 1.04, 95% CI: 1.01-1.07, p=0.0053). Higher AST/ALT ratio quartiles were associated with Higher prevalence of DR (OR vs. Q1: Q4 = 1.34 (CI: 1.03-1.75, p=0.0303).The GAM and smoothed curve fit indicated a linear relationship between AST/ALT ratio and DR risk, with no significant interaction effects across different subgroups.

Conclusion

Our study demonstrates a positive correlation between the AST/ALT ratio and diabetic retinopathy risk in type 2 diabetes, suggesting its potential role in assessing DR risk.

Predicting the risk of diabetic retinopathy using explainable machine learning algorithms

BACKGROUND AND OBJECTIVE

Diabetic retinopathy (DR) is a global health concern among diabetic patients. The objective of this study was to propose an explainable machine learning (ML)-based system for predicting the risk of DR.

MATERIALS AND METHODS

This study utilized publicly available cross-sectional data in a Chinese cohort of 6374 respondents. We employed boruta and least absolute shrinkage and selection operator (LASSO) based feature selection methods to identify the common predictors of DR. Using the identified predictors, we trained and optimized four widly applicable models (artificial neural network, support vector machine, random forest, and extreme gradient boosting (XGBoost) to predict patients with DR. Moreover, shapely additive explanation (SHAP) was adopted to show the contribution of each predictor of DR in the prediction.

RESULTS

Combining Boruta and LASSO method revealed that community, TCTG, HDLC, BUN, FPG, HbAlc, weight, and duration were the most important predictors of DR. The XGBoost-based model outperformed the other models, with an accuracy of 90.01%, precision of 91.80%, recall of 97.91%, F1 score of 94.86%, and AUC of 0.850. Moreover, SHAP method showed that HbA1c, community, FPG, TCTG, duration, and UA1b were the influencing predictors of DR.

CONCLUSION

The proposed integrating system will be helpful as a tool for selecting significant predictors, which can predict patients who are at high risk of DR at an early stage in China.

Diabetic retinopathy in China: Epidemiology, screening and treatment trends-A review

Diabetic retinopathy (DR) is the leading cause of vision impairment in the global working-age population. In China, with one-third of the world's diabetes population estimated at 141 million, the blindness prevalence due to DR has increased significantly. The country's geographic variations in socioeconomic status have led to prominent disparities in DR prevalence, screening and management. Reported risk factors for DR in China include the classic ones, such as long diabetes duration, hyperglycaemia, hypertension and rural habitats. There is no national-level DR screening programme in China, but significant pilot efforts are underway for screening innovations. Novel agents with longer durations, noninvasive delivery or multi-target are undergoing clinical trials in China. Although optimised medical insurance policies have enhanced accessibility for expensive therapies like anti-VEGF drugs, further efforts in DR prevention and management in China are required to establish nationwide cost-effective screening programmes, including telemedicine and AI-based solutions, and to improve insurance coverage for related out-of-pocket expenses.

Artificial intelligence for diabetic retinopathy in low-income and middle-income countries: a scoping review

Diabetic retinopathy (DR) is a leading cause of blindness globally. There is growing evidence to support the use of artificial intelligence (AI) in diabetic eye care, particularly for screening populations at risk of sight loss from DR in low-income and middle-income countries (LMICs) where resources are most stretched. However, implementation into clinical practice remains limited. We conducted a scoping review to identify what AI tools have been used for DR in LMICs and to report their performance and relevant characteristics. 81 articles were included. The reported sensitivities and specificities were generally high providing evidence to support use in clinical practice. However, the majority of studies focused on sensitivity and specificity only and there was limited information on cost, regulatory approvals and whether the use of AI improved health outcomes. Further research that goes beyond reporting sensitivities and specificities is needed prior to wider implementation.

Direct medical cost and medications for patient of diabetes retinopathy in Beijing, China, 2016 to 2018

AIMS

Medications and costs of drug for diabetic retinopathy in outpatient in China have not been evaluated. The purpose of this study was to evaluate the hypoglycemic drugs and medical costs of diabetic retinopathy patients in the Beijing medical insurance system, analyze the characteristics of outpatient treatment, and investigate the changes in the quantity and cost of hypoglycemic drugs from 2016 to 2018 METHODS: This is a retrospective observational study, including diabetic patients with outpatient records in Beijing medical insurance from 2016 to 2018. Data on oral hypoglycemic drugs , insulin and non-hypoglycemic drugs, complications, treatment strategies, and annual medical costs were recorded Results: A total of 2,853,036 diabetic patients in Beijing medical insurance were enrolled in this study. 4.19%-4.67% of patients were diagnosed with retinopathy. Patients with retinopathy have more diabetic complications (1.65±0.71 vs 0.18±0.44. pp<.0001),and use more drugs (5.11±2.60 vs 3.85±2.34, pp <.0001), the annual total drug cost is also higher (¥ 13836±11244 vs ¥ 10030±9375, pp <.0001). The numbers of medication in retinopathy patients increased(5.11±2.60 vs 4.95±2.57, pp <.0001), and the annual total drug cost (¥13836±11244 vs ¥15642±13344, pp <.0001)decreased in 2018 compared with 2016.

CONCLUSIONS

Patients with retinopathy were associated with more complications. Compared with patients without retinopathy, the number of medications and total medical costs were significantly increased. From 2016 to 2018, there was an increase in the number of medication treatments for patients with retinopathy, but a decrease in cost.